{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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BYiwd0NOfjVg1Iu46PbLfaGVxocSeSbr9jGZOsjjVnzRL0hUBpGK2mMgdVGLu6mMWTyq9Oj+Olb0gVoZfVdUrfuFS9LS8LPuLn59V73njFsqDGW5JsHVlj/g+0+2clbSlch8ofy6v67dx2camX6kTYq8JE86BAAiAwF+CwP4Se+Z+K3sjuRc7Ijjcqgiv1B2d5vsBO/hQLiebtEOIobToxJb0dNP+4Dd+d5p+QXFF7JmCtM1S72jFnhHV7p6uyopTrd5Rrew9X8y+DsH3v/HLsktfkDBp7jwPD7HIQxp8ebXrHwPmlStT6qGQlsu4Tf5lwalFaFP62jl5b2SXT8jJl1um6ESX9D5NwxfjambhOAiAAAiAwB4nsH/Enl3Rcqs8J+b904giOLwgUa9Qmby47B8AGcYDPYi9wTOa4Scho/vIGsSebpIwGKXYM5fK/Ypa8iSrVC31HqOba3KURv00ripZNjvfs2dyqHsLzWV+/SJkLtCsrOX/Dej1H/6+SHuS/aHFr4oXFpMj+I1WSpVh3O78wYrhVz9rZdTqVmZgEwRAAARAYJ8R2B9iTwm9aNVOBIeagNV9bUf/fptW+SnMTo5Rk3flklu1mJIt1cT6hHn1i7vfUK4Y2tMsLlpEnNTbks6W2bF98mDDBH1h7x/U9vrttXl/T51ib06NaGWvUKMcYtHTZaUsegjn3/GDHq5Acyl/go4e/4auPmx6QIH9odureI5A5LEAqwoufuVMck9gfh/gB5L7BA9fp0cd0nMZo7yULA7DBgiAAAiAQK8E9r7Ye7tCV/ihhPQSnAgdPQETkVzunaDhPlWmJu/PJvaI6M9FupI9BMHiokXECYOWdDbMurWP71c7dJifYi3EaE3U1Y5HRYS2dRFsJivf17b9/g3d5wc0vud3xc3RpXP+03jHzetDnGiLhJ4VY6f8U7namPAgSvM3d9nmJNZ0Udl2uBfz6NSUE8cn5+jpUH+AZIV2OoB79jphQiIQAAEQ+EsQ2NNib/O3eTrLlzinLtL9t4lPROgUJuC3y/TdFF/eOkanf1jq8NmybmIoscLtNtlSzJAc1Pfh2VMsLlpEnNTLbe34WxOz5h2IX/oyvl6kjcRM3g33wiXsRex1s4OFGZdr7pt88MMZOv3Vcb9y2K0cXhUzv6bMwe+3Q+ycPEf/4D8Y0vfPyWXeBmEb+SNpbzA82RrQy9vfuDZMTtP96KsbO3/pdlJJdRdir4oGJ0AABEDgL0dgb4o9816xn83H5v1Ef/JHepAKPeMqETqVCXjzMc18xe+mM2VN0aWVpkt1PYq9LPT6EXvmSw1OODW/dFi+QnE4+ZTarsUeET/QoAWc+VLF1Fdu9e6EF/Hh1SuvaIMvVX4c0PZv8+GzdX41OLx2JXlxsFyOvk6P8gdplVfYH5VYUylp+xXd/a//nJq+T/CTWZX0302ePLOzz+npehq2IfYa4OAUCIAACPzFCOw9sbe5QpdkRW6CGu+7axN7xplmgv3BC0fzupbGCX0XYu/Vbf95sw5ioFOQsbjovrJ39aH/jBgLn+z3Fd1p+kKIvvx98ja9NKuNgze0+mSNXr/jsrfo9fL18DLqdKVMxN45uvOK86S//G45/fRxgDJ4u0ZPn7CAyx3WdM/exvKPwbaT1+mRvI3Z3JvHwv8UzfjPzolo/edSdRXTWcb+aPDv+3V6dEt/euwMzeivndh4jP+QOfrVZVp4uF79hrJjkb9LsPQ6H33s/s9nvGi/SAtdXx8UpVujjRx9cBK2QAAEQAAE9g2BvSf2iGjzsRETx+js/DPazC5vKrZdxJ5PvvnkF7rT+sLlDmLv3TNaMJ/DWliiRzw5PlyiGfm6hfo0lzJ1+E0WF93FXvXGfqm8oX3qSWazihberbdGN/lSOq+0ym9h9U/EXoMokteYlMWemFvZKIo9LeoPT9ivTWT3xr1blnftmVXDySm/yna44UEUsYH9Ebdr8G6dni7N06W/x5edJ8/dpqfvJHO2YUTp6YjrFJ3493VaWHpMT9fD08FhhW74y9nxyuiw+eN2Zg3AARAAARAAgX1DYE+KPUPPPB3Y+t8QYq+1LJugQQxxAepJ1dJkerT0aTHOO9Qvi4vPI/YGa7dlRSxug3taNW/rMTrNr8DR7epL7KlL9if+2/DanT/5XY1K/Nh76nQjStvsjyCCXt/5xq+ehbImv75MC0+abhVQZW+/ovtz52hSxPMEHfpyjp7al2q7dNsN7xvk9w6O53dxyCfZVbuwCQIgAAIgsKcI7Fmx14lSH2KP1ukOP/l5kleGzP1k5+jSQstKZKdGcSIWFy1iz15mdZf5XtvPenH+0u+ANtb8JcE/cjG98euPdCJ94tkU8/EDbfyuLyW+og0lSKKa3q/7T5g1XQYMXyaJvyQSlVTdKa7sWTvX6O7SeuvXMew9dT+dI3Pv39Hj07Twe5frleyPIPZosEYz5mGWqW/o0vyS+kxb1fTyiW2zOjhH549PqRXVclIcBQEQAAEQAIFhCewBsTesySr9p4F8a/TgvR8sCKJ2EaeY7Haz6bL5bsve5/n59S9RrA26CMV93nCYDwIgAAIgsK8J7G+xt6/Rw3gQAAEQAAEQAAEQGD8BiL3xM0YNIAACIAACIAACINAbAYi93tCjYhAAARAAARAAARAYPwGIvfEzRg0gAAIgAAIgAAIg0BsBiL3e0KNiEAABEAABEAABEBg/AYi98TNGDSAAAiAAAiAAAiDQGwGIvd7Qo2IQAAEQAAEQAAEQGD8BiL3xM0YNIAACIAACIAACINAbAYi93tCjYhAAARAAARAAARAYPwGIvfEzRg0gAAIgAAIgAAIg0BsBiL3e0KNiEAABEAABEAABEBg/AYi98TNGDSAAAiAAAiAAAiDQGwGIvd7Qo2IQAAEQAAEQAAEQGD8BiL3xM0YNIAACIAACIAACINAbAYi93tCjYhAAARAAARAAARAYPwGIvfEzRg0gAAIgAAIgAAIg0BsBiL3e0KNiEAABEAABEAABEBg/AYi98TNGDSAAAiAAAiAAAiDQGwGIvd7Qo2IQAAEQAAEQAAEQGD8BiL3xM0YNIAACIAACIAACINAbAYi93tCjYhAAARAAARAAARAYPwGIvfEzRg0gAAIgAAIgAAIg0BsBiL3e0KNiEAABEAABEAABEBg/AYi98TNGDSAAAiAAAiAAAiDQGwGIvd7Qo2IQAAEQAAEQAAEQGD8BiL3xM0YNIAACIAACIAACINAbAYi93tCjYhAAARAAARAAARAYPwGIvfEzRg0gAAIgAAIgAAIg0BsBiL3e0KNiEAABEAABEAABEBg/gb0j9t7dowtHZunFLtv84sYRurC0NVwpz2fpyLf3KM71gmaPHKHZ58MVZeo/cmO3rRiuzq2lC3TkyBH7z7ZdsbTnGuyx523bTXuH5W/yXKB77xJ7izyTNKPYNfUMbbOqWHFSR4faDPyGykYvbhS4DVGEqbdbbFZ8xHWNylejKoft6vS7Rfe+3R1HU03Rh9IeU8eRwvgQDLT5bf/rbovJM/Q4FaqMtnZcViX+4zHUtb9brEVmxTuVuuJE3feMjcPaFLerUtewdkqcVMprOtxSV6u9lfxxvnH7rzJPKtuG8pXK14QO54YnsGfEngmIUYikONA7Ail22EoQtxQ5qnY0VmPs9eLO/qZCVXWYrXcv3GRVFEVuIJBJx5brBV9ah6kvrYcKQsLW3TLp2TROnEbt0G0qbUf1+0k4FbJNZUf5iajo90byyckwkFq/l2xOj7ENmrXl2M5D/GSs6MLZWlvwkWpF6C8u3hv9kbJOyhlF/1VFdtg0/FtirVJKo7+MjyQ2tmjrHZFLz38MJazYp5W6yodTvyRlpnFj97l+VaKNI2bQXkYkkGwM5WWGmDD1hBhXtXoeDTFrYkUYcrz6ump99MaL9nL5j0tfhukTQWwn9hgbVBvjdunWqG2VXh2tboYy29ln/aOlrlB2pfpK/jjfGPwXmePaHcWVOS+2Gf/4+aph/JAio3FRjmJjBAT6EXvWoUnHLA5uhTQ8iHRNn6Sbfe6Cv3FSS/LotNGEW3CA6WhZpy6kG9UhO9Clk410tFBL0S6bjicKl9aWV+qUeuCWYk1HV/lteQWfCc98YpGiahuqzGhAyWIoLjtrR8H+LI23wR4Xm1V7Us7WhrjecjMaBtwSa10Itz+tu7YipfPa7cRH0Xl9Tm9HiexOxIptKjFqPdaFV15/+YjhquJPJar5MIohn96mTfkW4kUVL5ulvLW6zTgS1d85fqS6eMP6odz+OGFlz+b3/ujsU+2/JK5TZnpf15Wa4+uO2Jg0On+ah88fmaV75spG4j8Z71S9kQiy7FXfbo1bkzZlrfuM3s6NtTGR9nVlm83RalNSv87vGeq5qrw9Av/5upy/TLuTuDaN0bZl+zufg9vm35w8jjCBfsQe1+5/bcdMOmuSpNuu6izZwNFUQnFQqQSxtrnTAKEGlLSzN9nU8VxpsiG7UqQ7dbmwaABKO2eaRTEKg6Ya4Cz7C3Tv+VZyOZw7/rCX19WAkMZGNNi4VZd0EJDBntsh9ju/Ng2EJaalY4EDV1L7TSZFTpYxd+m4LbbO0kDK+WVlVbFqi0nFMvK/lCWFRxtx2uhU+TJonGS0e97/RR/69uX2Kh/YWFX9so1ZdD6ecG09iqlpaF63PWpX2NNxycRQeqwTLO5v6S0UnTL7RFn8ueNxXCtuWdnJOeljPqHeT+sy+4ab92WJQWxHVrkcCD4I45HJa//oVvXm5SX2mxJVeq7A5CvZF/s51M359G+Uthh/+Xid26tLLNtqUsT5Cm2UYpJz2l8mjd5PuMTMC3yS9FJl04bNc4EufFsorykfznUisAfEnpt8084UB2yXtphyZmnW3LN3Y9YNJF2ymTQ6qCVP2S4+XbPPHC+t7EWdnQvZ0a/roMWJLpqUKpOZTEyufSwsSoNcZF7EiAcJU4YReOF+S9tOEShsazxBRuUWdrgM08Y0Lkxyy7jYVq4n913O39vOk6UanGxa4eQMzI7Z9ErAGj6ZTWwP8yo0NjpkLhk6221ZiQ1R0s47STsln69H/gCppXMZcn5ckCun5CdOMb7fpG4Vo7m9NR8kZbCxKh74kPwWfe37240XQ4k9KXOoDWOzEgfG1mFipWh/KC8e2wrcfOzn8a7HnAt0z1y+Y7sKPK2Pjqg+ZBi0lG3jTPnZZAl903BxfU7GYVVv3C4P3LOQ+FXpTQo31szS7PP0PmwXN2GsD3X7kqOfPB65rYF7lCETbersXvCf95PhYlf2lswcoP1f2ZbxRrXHbrpy3HxUiLk0OfaHJtC72AsdNba92DHjJNEep+dfU6504CjlaHa4nrQ0czwMAOFssbOH0zveKvMznYWFRqVoP2DsTOxxmZUBTgajFhu4GP3LeXmS0OfUtmm3td0MOjZtaHPwgRtA3KSU2GLrUQOt2fd1lpjGx9xgZMoVfml50UpZ++BlbW4bLHmgZEYmPR+z91ap9girso9se6L8mlX3gdrabbl1yK8Fiti3mw1Xp/Tz1IfCxtRR8YHKE1tiyg48Y/9zSh8HSazatFHdDfXborqw8z5J6nKr+CoO2bQOvy4GkjY2xmBI64o3dqt+lbLU+4mIYvNCX/VHKuli/8Xcg2+CPVKuKs8ck/7KBpjfmp36uE7PAnO3/UfZ1iZyg7AOvPv2n2M8W76Ma9jp2Ej4Rbs2rYnv0DZzXnwYJcbOTgn0K/ZssNdXb4ods9RS1SlDh44H6zjbEIMrD37JIBvqSUreM2Iv4WoYyQQU2i+M9cATN8ntKcbhNA+ufvBlVlKPSenOST0hc3nLdvx0UsmTmoHOlmnstr4x9Vyg2RsXVDvzfHzEDpTKTr1vtxN/62O8fU9PHpndzMbU6BiwKLGDmGelj2lG2h5bgrkvSdmbH/M+SNI4MRAPoryKa1bBQ5na3nzVIbXHcrRtTsq2J9T/fB83kxW3VZ3d5aaLYylXxWhur2MNFEEAAAwiSURBVPaByxcm0FTcmjZpHi6v9o81nNtWipXMD6p+y60m3vRKVTueMCF6/3MfrP6yvzh96Gsc1/FbCbwNhfEhS6/4Z5ar/DZfyT7DUaWLY1fxs4W7feOTYEfwmXBR5dXG7MhWlT46rnd8ml33ny516Xqj7f78F5lh/6jN+zb7OOszOrNtv+kHPgZ9v5D+bNL6Y43l6DKxXSXQo9iLg1VPgNVBOBlUbatswPAAltyv0DT4REjSgSQ6Gf/l50/VBg4ZZJIibPBnE0CSaAe73KkCM8ciso87kRkY+a/Sb+9RJFbaBp4iyzC41k0Pg3I9jTpjbQ0TkDoTbebtVn8ZqpiIOEgJuU06nS07iTV9jF+bovO4QUnbrdmU4is+ZsoKPqxsJ/FjbYqO6Tq5sYVjz81lqZa/nNviwQ/y6V/jXKv5dW0KfVOfG8120jYVozmbmLepP/g0Kccap9JXYlJ89q25zyi8uimv29aW37On7A08jC1h8jRlRZNfSCi3M9TO26SqL6is7nKpsVv90WHtLokwOabi25Yb7LRlF9vjay3GU8J4KLEXWlPyo/WN6RuqXnPMigZve2t/k3ar/mhsHEn/4T+ofNlRPw5tM1vFeDJt6N1/3MeTOOBL0N/mD87YljF/1WekxcpfcowZGH80cNLpsZ0T6E3shYFFDSDKPumY6li+GQ+M5nyaTzp9nlkdUYOOOiqbhUHMllsaDJqOjShQs7oLnYYHCOasJ4SiWKl0snYGbZO5Y9v5L7PKxCp2+A3TLhm4bftNPUHo1gZyy8G2VdsdT/aWWcK0dCyKtcxuXWYpvuJjUVk8uKl4sfWrfYOheiyyXdsRU7RxZMq0tqsJrSmG7aUWZ/uRG+YSjuaYl9/Z73HWbnupH007PKOcTczbiQC2vczI+cS9uihvh8tjV5KFlxvL8rpNc5L6zSFjb+QrLUA9gmK/9Pz9ZK/7dgYuZeQTmLbN2vusjM2mPL8qntgj5UV2uPpzJpLab3g7mU8Sv5qJMIvq0X5xZZXaavNau0N6iW1VnvNncd3S2WvTtosvbqXUMWz/WdH395bnP67D/AobdXAv+M/xvBf9ceJMZD/wrzIcm70R6Efs+U41e8MEfTnYWzumX1lIB5w8nx9wsoFGM68PJCZVrbOldZu0pn6ecKIaCpfh9PmdboeBLilBBi6e0JLzqTBWg2KesjAxyQBnyjeduqNYqE0mXKkttxwTnMT8mnZb/sZuW6bxIT8sEtqcx4MvxfOxZSSTbolp6VhUtvDQHNiOUnzFx2zctDFMYtjalBxzEyjXa9paH3CbYtU+5HTErFgVfCG8fNn2AR3d7qZtbZv26A62k1jRPPS2Kznmbc/XePsYDWlyBly+/fXpmSefi1sU12/PCUdOWfZVVp7kc/2uJIC4xFjU+qPcZ5I+b+upMbHHcw5SD2+IbXyg6ZeZuF/pzzwnRPZx2lAe9z/xgUrPviiu7IUiwhbn5d/ns258CSmyLakjOWPsaew/nJ7rMvt2u6nf8LlZemHSmpjT+Xme+kz+C23PY1D8wTZlY5RpsPI58/B/dGQv6Zfz2NgNgV7EngkUO0Alg7VuiElTElM2DXeMQhCV87mALIkwV2c+kGhbbPAW6tJpeDt0Aj4y3l/dsUJNvr3qcpA5l6aNWCUDRyjLb+lB3PrN+NDUoyZvHoSizKVOHSWIdxpiQie0nLOBTdniE0dt1AXY7cBJx1rKySQtHYvKbrS7FF/xsagsrk/FnK1f7YtNybGsidavOReTzjJM8osdEg+GUW2ST/yfVC5lJcdHtZvarxnpbVdfzDu2odIOyyC/RKUFVCku8rpNbYX6dZ/yPtdxqO02K9W5qHPxmx9XrSv531+G1EJI5fCbFSaS0LWntoJePJ7EWpGJxF36B2bKz+1rXiVf6DZW41H3XVV/2Y8CYPf9R9UVSs23Mjv69l8Ut2kMtu379um4lPLCC5ijmO7IKSeHI5pAL2JPDNCdTA66jWrHTCcpWwb/1VP65YnOBaEeHEKV6UASzvCAVM6n0wW766Jy9+nTEtIBzk6ARgTxKyD8qoPJl06OEeO2DqU6pPl8lOuM+YSQ2jMsv/zet7TFbt/Ybm0wdts2Gh/61z1kIlDFheLBttmJSR3P29BB7JXN9EdL8RUfi3yxW7FnfananE2ygaHEqs8jcR7FQ63v5P7XGNI26XPWzxW7onTVHWeTnhT0pKi3XREx77hYV5YIFIknx1DqsEwu0L2VsOpTjZWsbYX6pU91XNlRMerszxnE7eJyeQxMziof23Y09Rs5VxP+vmzdpqS6fLfARGxy54xPHP8kLfuCX53kxbT1Ycbe1VyKR3Msurok9bs8jku5zTYv12XzqQWKqBznJ+lbDCJKYw6W/ZnHsi9A5e/Hf6nN3mfMhNtp5+iYYcQujRnTLh/rNt239+iF/SSojuMQHzKGcX34LRLYl2Kv2BJ/0ARH1qmaMthz8UBiA0wGt9JnwuoFRkFcT+bPlDt3azaVQAYC2/HTS8i688VtNEVErNTAoYoPm2mHtGeM/boDuuSmXJkgi8v1odh0y7GPB4Y0TXRpUgYG076yLbV4sHXpQcWvYFUHzmSyjfjlRqojBT97f7FtaVnOr/f8p+4S0WFKZn9XV91U9ZVN2375RFXCvBAPjotOV/Y/V5e2iY+bX3MJyl76TycGnahpuzKBMM+aD11cOn+IuLN9PY4dy4YnGfZ7oU5bD5/39tbqDsLFEog+Y8h5Qr9JG1+K70JcpdmsH+O2SZKCj/mci41Zmk3axuerv8VxopY6H5NcSnfc3s/IcZ7akdTD/jKPoNlvGRfiKopHLjdNV2JSSesY8SfedL/g/hkfcz5Wx0p1FW5PsvlSOw2oYn5H8PP4z9TFMah85kyI/m/t4bHK85RYT3wZMnLZ/ojqf4FJLYZCKdhyBPaE2LOfu9Hiqmm7FPTKm1GHVsebN0cXMDwANNfnzzZ01ub88WQlnaaSyXW05C/YIcWe7VzpgNtwP1gwxbHlSTgc91u2Azsxw5NvW3tKN7a7Vbp8UivHQ3lgYt+V2lo6Vi7bt8sPaNym6EZ832Z+IWmVTQYrLtvks3bJ6kctQ/k4t7d4tlNsmjiMmdsypf/G5/J6XBwP3X49IUbxEyZSyyUaK5r6eNwO1wYuK+TLyyyv+JbS8SqyjW2ODWOfxEJOp/2I49fYX2z53JakxJKPvT3iE2+r7JsiIuZx35V4lxhIz2tbCvZz2ZHv/FUJFWuh75X6sj8WleGO2XbYOiqxWWLiscVxkV8pieg2lCPprB2ah5yJNsrxVBF7np/4a6z+M2Y6H1q/Z/ND1Aw/VpXaq8pI46ZSZmAS+mdcG/ZSAntC7KXvJk+NHP/+6ALGDgjRIFO3PgRsPc04z4QBszRw+AFTdb58UjGdtDxouoGRB/pSB995y0zZwRZlZ2FgiNpoq3TpZTAsmGH9kpRVOlbI2ulQbD9PZMyq5dfElhnAdYz5Ab19ojVlj9YXnRrclKjLpJjkH32/UXFsJsvE93pCC3GXGDXU7gua1f7TE6bqb2V/av+5STK1Ke576Wq/MlTYqz4U2RXSWubWNl1/OD/UVhSvXB7bwPuFEkUcmbRm3PF5ijZ7n0YPDzWUzdUJEz4whl/d/qLtuaBOfWytEluZXd3XI/XfGJAMX6SLfdtHagyHL/RA5+hX7B1otGgcCIAACIAACIAACPRPAGKvfx/AAhAAARAAARAAARAYGwGIvbGhRcEgAAIgAAIgAAIg0D8BiL3+fQALQAAEQAAEQAAEQGBsBCD2xoYWBYMACIAACIAACIBA/wQg9vr3ASwAARAAARAAARAAgbERgNgbG1oUDAIgAAIgAAIgAAL9E4DY698HsAAEQAAEQAAEQAAExkYAYm9saFEwCIAACIAACIAACPRPAGKvfx/AAhAAARAAARAAARAYGwGIvbGhRcEgAAIgAAIgAAIg0D8BiL3+fQALQAAEQAAEQAAEQGBsBCD2xoYWBYMACIAACIAACIBA/wQg9vr3ASwAARAAARAAARAAgbERgNgbG1oUDAIgAAIgAAIgAAL9E4DY698HsAAEQAAEQAAEQAAExkYAYm9saFEwCIAACIAACIAACPRPAGKvfx/AAhAAARAAARAAARAYG4H/By8jfyOHwdxWAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "`Learn the Basics <intro.html>`_ ||\n",
    "`Quickstart <quickstart_tutorial.html>`_ || \n",
    "`Tensors <tensorqs_tutorial.html>`_ || \n",
    "`Datasets & DataLoaders <data_tutorial.html>`_ ||\n",
    "`Transforms <transforms_tutorial.html>`_ ||\n",
    "`Build Model <buildmodel_tutorial.html>`_ ||\n",
    "`Autograd <autogradqs_tutorial.html>`_ ||\n",
    "`Optimization <optimization_tutorial.html>`_ ||\n",
    "**Save & Load Model**\n",
    "\n",
    "Save and Load the Model\n",
    "============================\n",
    "\n",
    "In this section we will look at how to persist model state with saving, loading and running model predictions.\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.onnx as onnx#部署模型使用的onnx\n",
    "import torchvision.models as models"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving and Loading Model Weights\n",
    "--------------------------------\n",
    "PyTorch models store the learned parameters in an internal\n",
    "state dictionary, called ``state_dict``. These can be persisted via the ``torch.save``\n",
    "method:\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = models.vgg16(pretrained=True)\n",
    "torch.save(model.state_dict(), 'model_weights.pth')"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To load model weights, you need to create an instance of the same model first, and then load the parameters \n",
    "using ``load_state_dict()`` method.\n",
    "![image.png](attachment:image.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = models.vgg16() # we do not specify pretrained=True, i.e. do not load default weights\n",
    "model.load_state_dict(torch.load('model_weights.pth'))\n",
    "model.eval()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\"><h4>Note</h4><p>be sure to call ``model.eval()`` method before inferencing to set the dropout and batch normalization layers to evaluation mode. Failing to do this will yield inconsistent inference results.</p></div>\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving and Loading Models with Shapes\n",
    "-------------------------------------\n",
    "When loading model weights, we needed to instantiate the model class first, because the class \n",
    "defines the structure of a network. We might want to save the structure of this class together with \n",
    "the model, in which case we can pass ``model`` (and not ``model.state_dict()``) to the saving function:\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model, 'model.pth')"
   ]
  },
  {
   "attachments": {
    "image.png": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then load the model like this:\n",
    "![image.png](attachment:image.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = torch.load('model.pth')"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\"><h4>Note</h4><p>This approach uses Python `pickle <https://docs.python.org/3/library/pickle.html>`_ module when serializing the model, thus it relies on the actual class definition to be available when loading the model.</p></div>\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exporting Model to ONNX\n",
    "-----------------------\n",
    "PyTorch also has native ONNX export support. Given the dynamic nature of the\n",
    "PyTorch execution graph, however, the export process must\n",
    "traverse the execution graph to produce a persisted ONNX model. For this reason, a\n",
    "test variable of the appropriate size should be passed in to the\n",
    "export routine (in our case, we will create a dummy zero tensor of the correct size):\n",
    "![image.png](attachment:image.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_image = torch.zeros((1,3,224,224))\n",
    "onnx.export(model, input_image, 'model.onnx')\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a lot of things you can do with ONNX model, including running inference on different platforms \n",
    "and in different programming languages. For more details, we recommend \n",
    "visiting `ONNX tutorial <https://github.com/onnx/tutorials>`_.\n",
    "\n",
    "Congratulations! You have completed the PyTorch beginner tutorial! Try \n",
    "`revisting the first page <quickstart_tutorial.html>`_ to see the tutorial in its entirety\n",
    "again. We hope this tutorial has helped you get started with deep learning on PyTorch. \n",
    "Good luck!\n",
    "\n",
    "![image.png](attachment:image.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
